Table of Content 1) What are data analyst tools? 2) The best 17 data analyst tools for 2023 3) Key takeaways & guidance

To be able to perform data analysis at the highest màn chơi possible, analysts and data professionals will use software that will ensure the best results in several tasks from executing algorithms, preparing data, generating predictions, and automating processes, to lớn standard tasks such as visualizing và reporting on the data. Although there are many of these solutions on the market, data analysts must choose wisely in order to lớn benefit their analytical efforts. That said, in this article, we will cover the best data analyst tools và name the key features of each based on various types of analysis processes. But first, we will start with a basic definition và a brief introduction.

Bạn đang xem: Phân tích dữ liệu đa nguồn (Multi-source Data Analytics)

1) What Are Data Analyst Tools?

Data analyst tools is a term used khổng lồ describe software & applications that data analysts use in order khổng lồ develop and perform analytical processes that help companies lớn make better, informed business decisions while decreasing costs & increasing profits.

In order to make the best possible decision on which software you need khổng lồ choose as an analyst, we have compiled a menu of the đứng đầu data analyst tools that have various focus và features, organized in software categories, và represented with an example of each. These examples have been researched và selected using rankings from two major software đánh giá sites: Capterra và G2Crowd. By looking into each of the software categories presented in this article, we selected the most successful solutions with a minimum of 15 reviews between both đánh giá websites until November 2022. The order in which these solutions are listed is completely random và does not represent a grading or ranking system.

2) What Tools vày Data Analysts Use?


To make the most out of the infinite number of software that is currently offered on the market, we will focus on the most prominent tools needed to be an expert data analyst. The image above provides a visual summary of all the areas & tools that will be covered in this insightful post. These data analysis tools are mostly focused on making analysts lives easier by providing them with solutions that make complex analytical tasks more efficient. Like this, they get more time to lớn perform the analytical part of their job. Let’s get started with business intelligence tools.

1. Business intelligence tools

BI tools are one of the most represented means of performing data analysis. Specializing in business analytics, these solutions will prove to be beneficial for every data analyst that needs lớn analyze, monitor, and report on important findings. Features such as self-service, predictive analytics, và advanced SQL modes make these solutions easily adjustable to lớn every level of knowledge, without the need for heavy IT involvement. By providing a mix of useful features, analysts can understand trends và make tactical decisions. Our data analytics tools article wouldn’t be complete without business intelligence, & datapine is one example that covers most of the requirements both for beginner & advanced users. This all-in-one tool aims to facilitate the entire analysis process from data integration & discovery to reporting.




Visual drag-and-drop interface to build SQL queries automatically, with the option lớn switch to, advanced (manual) SQL mode

Powerful predictive analytics features, interactive charts và dashboards, & automated reporting

AI-powered alarms that are triggered as soon as an anomaly occurs or a goal is met

datapine is a popular business intelligence software with an outstanding rating of 4.8 stars in Capterra and 4.6 stars in G2Crowd. It focuses on delivering simple, yet powerful analysis features into the hands of beginners & advanced users in need of a fast and reliable online data analysis solution for all analysis stages. An intuitive user interface will enable you khổng lồ simply drag-and-drop your desired values into datapine’s Analyzer and create numerous charts & graphs that can be united into an interactive dashboard. If you’re an experienced analyst, you might want lớn consider the SQL mode where you can build your own queries or run existing codes or scripts. Another crucial feature is the predictive analytics forecast engine that can analyze data from multiple sources which can be previously integrated with their various data connectors. While there are numerous predictive solutions out there, datapine provides simplicity và speed at its finest. By simply defining the input and output of the forecast based on specified data points và desired model quality, a complete chart will unfold together with predictions.

We should also mention robust artificial intelligence that is becoming an invaluable assistant in today’s analysis processes. Neural networks, pattern recognition, and threshold alerts will alarm you as soon as a business anomaly occurs or a previously set goal is met so you don’t have to manually analyze large volumes of data – the data analytics software does it for you. Access your data from any device with an internet connection, and share your findings easily & securely via dashboards or customized reports for anyone that needs quick answers to lớn any type of business question.

2. Statistical Analysis Tools

Next in our danh sách of data analytics tools comes a more technical area related to statistical analysis. Referring to computation techniques that often contain a variety of statistical techniques to lớn manipulate, explore, và generate insights, there exist multiple programming languages to make (data) scientists’ work easier & more effective. With the expansion of various languages that are today present on the market, science has its own set of rules and scenarios that need special attention when it comes to lớn statistical data analysis & modeling. Here we will present one of the most popular tools for a data analyst – Posit (previously known as RStudio or R programming). Although there are other languages that focus on (scientific) data analysis, R is particularly popular in the community.




An ecosystem of more than 10 000 packages & extensions for distinct types of data analysis

Statistical analysis, modeling, and hypothesis testing (e.g. Analysis of variance, t test, etc.)

Active và communicative community of researchers, statisticians, and scientists

Posit, formerly known as RStudio, is one of the đứng top data analyst tools for R & Python. Its development dates back lớn 2009 và it’s one of the most used software for statistical analysis & data science, keeping an open-source policy and running on a variety of platforms, including Windows, mac
OS and Linux. As a result of the latest rebranding process, some of the famous products on the platform will change their names, while others will stay the same. For example, RStudio Workbench and RStudio Connect will now be known as Posit Workbench and Posit Connect respectively. On the other side, products lượt thích RStudio Desktop và RStudio vps will remain the same. As stated on the software’s website, the rebranding happened because the name RStudio no longer reflected the variety of products and languages that the platform currently supports.

Posit is by far the most popular integrated development environment (IDE) out there with 4,7 stars on Capterra và 4,5 stars on G2Crowd. Its capabilities for data cleaning, data reduction, and data analysis report output đầu ra with R markdown, make this tool an invaluable analytical assistant that covers both general và academic data analysis. It is compiled of an ecosystem of more than 10 000 packages & extensions that you can explore by categories, & perform any kind of statistical analysis such as regression, conjoint, factor cluster analysis, etc. Easy khổng lồ understand for those that don’t have a high-level of programming skills, Posit can perform complex mathematical operations by using a single command. A number of graphical libraries such as ggplot và plotly make this language different than others in the statistical community since it has efficient capabilities to lớn create chất lượng visualizations.

Posit was mostly used in the academic area in the past, today it has applications across industries and large companies such as Google, Facebook, Twitter, & Airbnb, among others. Due lớn an enormous number of researchers, scientists, & statisticians using it, the tool has an extensive và active community where innovative technologies & ideas are presented & communicated regularly.


Naturally, when we think about data, our mind automatically takes us lớn numbers. Although much of the extracted data might be in a numeric format, there is also immense value in collecting & analyzing non-numerical information, especially in a business context. This is where qualitative data analysis tools come into the picture. These solutions offer researchers, analysts, and businesses the necessary functionalities to make sense of massive amounts of qualitative data coming from different sources such as interviews, surveys, e-mails, customer feedback, social truyền thông comments, & much more depending on the industry. There is a wide range of qualitative analysis software out there, the most innovative ones rely on artificial intelligence and machine learning algorithms to lớn make the analysis process faster and more efficient. Today, we will discuss MAXQDA, one of the most powerful QDA platforms in the market.




The possibility lớn mark important information using codes, colors, symbols or emojis

AI-powered audio transcription capabilities such as speed and rewind controls, speaker labels, and others

Possibility khổng lồ work with multiple languages và scripts thanks to Unicode support

Founded in 1989 “by researchers, for researchers”, MAXQDA is a qualitative data analysis software for Windows và Mac that assists users in organizing và interpreting qualitative data from different sources with the help of innovative features. Unlike some other solutions on the same range, MAXQDA supports a wide range of data sources và formats. Users can import traditional text data from interviews, focus groups, website pages, & You
Tube or Twitter comments, as well as various types of multimedia data such as videos or audio files. Paired to that, the software also offers a Mixed Methods tool which allows users to lớn use both qualitative and quantitative data for a more complete analytics process. This cấp độ of versatility has earned MAXQDA worldwide recognition for many years. The tool has a positive 4.6 stars rating in Capterra and a 4.5 in G2Crowd.

Amongst its most valuable functions, MAXQDA offers users the capability of setting different codes to lớn mark their most important data & organize it in an efficient way. Codes can be easily generated via drag và drop và labeled using colors, symbols, or emojis. Your findings can later be transformed, automatically or manually, into professional visualizations and exported in various readable formats such as PDF, Excel, or Word, among others.

4. General-purpose programming languages

Programming languages are used khổng lồ solve a variety of data problems. We have explained R and statistical programming, now we will focus on general ones that use letters, numbers, và symbols lớn create programs và require formal syntax used by programmers. Often, they’re also called text-based programs because you need khổng lồ write software that will ultimately solve a problem. Examples include C#, Java, PHP, Ruby, Julia, and Python, among many others on the market. Here we will focus on Python & we will present Py
Charm as one of the best tools for data analysts that have coding knowledge as well.




Intelligent code inspection & completion with error detection, code fixes, & automated code refractories

Built-in developer tools for smart debugging, testing, profiling, và deployment

Cross-technology development supporting Java
Script, Coffee
Script, HTML/CSS, Node.js, & more

Charm is an integrated development environment (IDE) by Jet
Brains designed for developers that want to write better, more productive Python code from a single platform. The tool, which is successfully rated with 4.7 stars on Capterra & 4.6 in G2Crowd, offers developers a range of essential features including an integrated visual debugger, GUI-based chạy thử runner, integration with major VCS and built-in database tools, & much more. Amongst its most praised features, the intelligent code assistance provides developers with smart code inspections highlighting errors & offering quick fixes and code completions.

Charm supports the most important Python implementations including Python 2.x and 3.x, Jython, Iron
Python, Py
Py and Cython, & it is available in three different editions. The Community version, which is không lấy phí and open-sourced, the Professional paid version, including all advanced features, & the Edu version which is also không tính phí and open-sourced for educational purposes. Definitely, one of the best Python data analyst tools in the market.

5. SQL consoles

Our data analyst tools danh sách wouldn’t be complete without SQL consoles. Essentially, SQL is a programming language that is used to lớn manage/query data held in relational databases, particularly effective in handling structured data as a database tool for analysts. It’s highly popular in the data science community và one of the analyst tools used in various business cases and data scenarios. The reason is simple: as most of the data is stored in relational databases and you need khổng lồ access and unlock its value, SQL is a highly critical component of succeeding in business, và by learning it, analysts can offer a competitive advantage khổng lồ their skillset. There are different relational (SQL-based) database management systems such as My
SQL, Postgre
SQL, MS SQL, and Oracle, for example, and by learning these data analysts’ tools would prove khổng lồ be extremely beneficial to any serious analyst. Here we will focus on My
SQL Workbench as the most popular one.

SQL Workbench



A unified visual tool for data modeling, SQL development, administration, backup, etc.

Instant access lớn database schema và objects via the Object Browser

SQL Editor that offers màu sắc syntax highlighting, reuse of SQL snippets, and execution history

SQL Workbench is used by analysts lớn visually design, model, & manage databases, optimize SQL queries, administer My
SQL environments, and utilize a suite of tools to lớn improve the performance of My
SQL applications. It will allow you to perform tasks such as creating and viewing databases & objects (triggers or stored procedures, e.g.), configuring servers, and much more. You can easily perform backup & recovery as well as inspect phân tích và đo lường data. My
SQL Workbench will also help in database migration & is a complete solution for analysts working in relational database management and companies that need to lớn keep their databases clean và effective. The tool, which is very popular amongst analysts and developers, is rated 4.6 stars in Capterra và 4.5 in G2Crowd.

6. Standalone predictive analytics tools

Predictive analytics is one of the advanced techniques, used by analysts that combine data mining, machine learning, predictive modeling, and artificial intelligence lớn predict future events, & it deserves a special place in our menu of data analysis tools as its popularity has increased in recent years with the introduction of smart solutions that enabled analysts khổng lồ simplify their predictive analytics processes. You should keep in mind that some BI tools we already discussed in this các mục offer easy to use, built-in predictive analytics solutions but, in this section, we focus on standalone, advanced predictive analytics that companies use for various reasons, from detecting fraud with the help of pattern detection lớn optimizing marketing campaigns by analyzing consumers’ behavior và purchases. Here we will danh mục a data analysis software that is helpful for predictive analytics processes & helps analysts to lớn predict future scenarios.




A visual predictive analytics interface to generate predictions without code

Can be integrated with other IBM SPSS products for a complete analysis scope

Flexible deployment to tư vấn multiple business scenarios & system requirements

IBM SPSS Predictive Analytics provides enterprises with the power khổng lồ make improved operational decisions with the help of various predictive intelligence features such as in-depth statistical analysis, predictive modeling, & decision management. The tool offers a visual interface for predictive analytics that can be easily used by average business users with no previous coding knowledge, while still providing analysts and data scientists with more advanced capabilities. Like this, users can take advantage of predictions to lớn inform important decisions in real time with a high level of certainty.

Additionally, the platform provides flexible deployment options to support multiple scenarios, business sizes & use cases. For example, for supply chain analysis or cybercrime prevention, among many others. Flexible data integration và manipulation is another important feature included in this software. Unstructured và structured data, including text data, from multiple sources, can be analyzed for predictive modeling that will translate into intelligent business outcomes.

As a part of the IBM sản phẩm suite, users of the tool can take advantage of other solutions and modules such as the IBM SPSS Modeler, IBM SPSS Statistics, & IMB SPSS Analytic server for a complete analytical scope. Reviewers gave the software a 4.5 star rating on Capterra & 4.2 on G2Crowd.

7. Data modeling tools

Our list of data analysis tools wouldn’t be complete without data modeling. Creating models to structure the database, and design business systems by utilizing diagrams, symbols, & text, ultimately represent how the data flows and is connected in between. Businesses use data modeling tools khổng lồ determine the exact nature of the information they control and the relationship between datasets, & analysts are critical in this process. If you need to lớn discover, analyze, and specify changes in information that is stored in a software system, database or other application, chances are your skills are critical for the overall business. Here we will show one of the most popular data analyst software used to lớn create models & design your data assets.

erwin data modeler (DM)



Automated data mã sản phẩm generation lớn increase productivity in analytical processes

Single interface no matter the location or the type of the data

5 different versions of the solution you can choose from & adjust based on your business needs

erwin DM works both with structured và unstructured data in a data warehouse & in the cloud. It’s used to lớn “find, visualize, design, deploy & standardize high-quality enterprise data assets,” as stated on their official website. Erwin can help you reduce complexities và understand data sources lớn meet your business goals & needs. They also offer automated processes where you can automatically generate models and designs lớn reduce errors & increase productivity. This is one of the tools for analysts that focus on the architecture of the data và enable you to lớn create logical, conceptual, and physical data models.

Additional features such as a single interface for any data you might possess, no matter if it’s structured or unstructured, in a data warehouse or the cloud makes this solution highly adjustable for your analytical needs. With 5 versions of the erwin data modeler, their solution is highly adjustable for companies & analysts that need various data modeling features. This versatility is reflected in its positive reviews, gaining the platform an almost perfect 4.8 star rating on Capterra and 4.3 stars in G2Crowd.

8. ETL tools

ETL is a process used by companies, no matter the size, across the world, and if a business grows, chances are you will need khổng lồ extract, load, & transform data into another database to be able to analyze it và build queries. There are some core types of ETL tools for data analysts such as batch ETL, real-time ETL, & cloud-based ETL, each with its own specifications và features that adjust to different business needs. These are the tools used by analysts that take part in more technical processes of data management within a company, & one of the best examples is Talend.




Collecting và transforming data through data preparation, integration, cloud pipeline designer

Talend Trust Score to ensure data governance & resolve chất lượng issues across the board

Sharing data internally & externally through comprehensive deliveries via APIs

Talend is a data integration platform used by experts across the globe for data management processes, cloud storage, enterprise application integration, and data quality. It’s a Java-based ETL tool that is used by analysts in order to easily process millions of data records và offers comprehensive solutions for any data project you might have. Talend’s features include (big) data integration, data preparation, cloud pipeline designer, và stitch data loader to cover multiple data management requirements of an organization. Users of the tool rated it with 4.2 stars in Capterra và 4.3 in G2Crowd. This is an analyst software extremely important if you need lớn work on ETL processes in your analytical department.

Apart from collecting and transforming data, Talend also offers a data governance solution khổng lồ build a data hub & deliver it through self-service access through a unified cloud platform. You can utilize their data catalog, inventory và produce clean data through their data chất lượng feature. Sharing is also part of their data portfolio; Talend’s data fabric solution will enable you to lớn deliver your information khổng lồ every stakeholder through a comprehensive API delivery platform. If you need a data analyst tool lớn cover ETL processes, Talend might be worth considering.

9. Automation Tools

As mentioned, the goal of all the solutions present on this các mục is to lớn make data analysts lives easier & more efficient. Taking that into account, automation tools could not be left out of this list. In simple words, data analytics automation is the practice of using systems and processes khổng lồ perform analytical tasks with almost no human interaction. In the past years, automation solutions have impacted the way analysts perform their jobs as these tools assist them in a variety of tasks such as data discovery, preparation, data replication, and more simple ones like report automation or writing scripts. That said, automating analytical processes significantly increases productivity, leaving more time lớn perform more important tasks. We will see this more in detail through Jenkins one of the leaders in open-source automation software.




Popular continuous integration (CI) solution with advanced automation features such as running code in multiple platforms

Job automations to lớn set up customized tasks can be scheduled or based on a specific event

Several job automation plugins for different purposes such as Jenkins Job Builder, Jenkins Job DLS or Jenkins Pipeline DLS

Developed in 2004 under the name Hudson, Jenkins is an open-source CI automation vps that can be integrated with several Dev
Ops tools via plugins. By default, Jenkins assists developers to automate parts of their software development process like building, testing, and deploying. However, it is also highly used by data analysts as a solution lớn automate jobs such as running codes & scripts daily or when a specific sự kiện happened. For example, run a specific command when new data is available.

There are several Jenkins plugins lớn generate jobs automatically. For example, the Jenkins Job Builder plugin takes simple descriptions of jobs in YAML or JSON format and turns them into runnable jobs in Jenkins’s format. On the other side, the Jenkins Job DLS plugin provides users with the capabilities to lớn easily generate jobs from other jobs và edit the XML configuration to supplement or fix any existing elements in the DLS. Lastly, the Pipeline plugin is mostly used khổng lồ generate complex automated processes.

For Jenkins, automation is not useful if it’s not tight khổng lồ integration. For this reason, they provide hundreds of plugins and extensions to lớn integrate Jenkins with your existing tools. This way, the entire process of code generation & execution can be automated at every stage và in different platforms - leaving you enough time to perform other relevant tasks. All the plugins & extensions from Jenkins are developed in Java meaning the tool can also be installed in any other operator that runs on Java. Users rated Jenkins with 4.5 stars in Capterra và 4.4 stars in G2Crowd.


As an analyst working with programming, it is very likely that you have found yourself in the situation of having to giới thiệu your code or analytical findings with others. Rather you want someone to lớn look into your code for errors or provide any other kind of feedback lớn your work, a sharing tool is the way to go. These solutions enable users to chia sẻ interactive which can contain live code and other multimedia elements for a collaborative process. Below, we will present Jupyter Notebook, one of the most popular and efficient platforms for this purpose.




Supports 40 programming languages including Python, R, Julia, C++, & more

Easily nói qua notebooks with others via email, Dropbox, Git
Hub và Jupyter Notebook Viewer

In-browser editing for code, with automatic syntax highlighting, indentation, and tab completion

Jupyter Notebook is an xuất hiện source website based interactive development environment used to lớn generate & share called notebooks, containing live codes, data visualizations, & text in a simple and streamlined way. Its name is an abbreviation of the vi xử lý core programming languages it supports: Julia, Python, & R and, according to lớn its website, it has a flexible interface that enables users to view, execute and share their code all in the same platform. Notebooks allow analysts, developers, and anyone else khổng lồ combine code, comments, multimedia, and visualizations in an interactive that can be easily shared and reworked directly in your website browser.

Even though it works by mặc định on Python, Jupyter Notebook supports over 40 programming languages và it can be used in multiple scenarios. Some of them include sharing notebooks with interactive visualizations, avoiding the static nature of other software, live to explain how specific Python modules or libraries work, or simply sharing code và data files with others. Notebooks can be easily converted into different output đầu ra formats such as HTML, La
X, PDF, and more. This màn chơi of versatility has earned the tool 4.7 stars rating on Capterra & 4.5 in G2Crowd.

11. Unified data analytics engines

If you work for a company that produces massive datasets và needs a big data management solution, then unified data analytics engines might be the best resolution for your analytical processes. Khổng lồ be able lớn make chất lượng decisions in a big data environment, analysts need tools that will enable them to lớn take full control of their company’s robust data environment. That’s where machine learning & AI play a significant role. That said, Apache Spark is one of the data analysis tools on our menu that supports big-scale data processing with the help of an extensive ecosystem.

Apache Spark



High performance: Spark owns the record in the large-scale data processing

A large ecosystem of data frames, streaming, machine learning, & graph computation

Perform Exploratory Analysis on petabyte-scale data without the need for downsampling

Apache Spark was originally developed by UC Berkeley in 2009 và since then, it has expanded across industries và companies such as Netflix, Yahoo, và e
Bay that have deployed Spark, processed petabytes of data & proved that Apache is the go-to solution for big data management, earning it a positive 4.2 star rating in both Capterra and G2Crowd. Their ecosystem consists of Spark SQL, streaming, machine learning, graph computation, & core Java, Scala, and Python APIs khổng lồ ease the development. Already in 2014, Spark officially phối a record in large-scale sorting. Actually, the engine can be 100x faster than Hadoop & this is one of the features that is extremely crucial for massive volumes of data processing.

You can easily run applications in Java, Python, Scala, R, and SQL while more than 80 high-level operators that Spark offers will make your data transformation easy & effective. As a unified engine, Spark comes with support for SQL queries, MLlib for machine learning và Graph
X for streaming data that can be combined lớn create additional, complex analytical workflows. Additionally, it runs on Hadoop, Kubernetes, Apache Mesos, standalone or in the cloud và can access diverse data sources. Spark is truly a powerful engine for analysts that need support in their big data environment.

12. Spreadsheet applications

Spreadsheets are one of the most traditional forms of data analysis. Quite popular in any industry, business or organization, there is a slim chance that you haven’t created at least one spreadsheet to analyze your data. Often used by people that don’t have high technical abilities to lớn code themselves, spreadsheets can be used for fairly easy analysis that doesn’t require considerable training, complex and large volumes of data & databases to lớn manage. Lớn look at spreadsheets in more detail, we have chosen Excel as one of the most popular in business.




Part of the Microsoft Office family, hence, it’s compatible with other Microsoft applications

Pivot tables và building complex equations through designated rows & columns

Perfect for smaller analysis processes through workbooks & quick sharing

With 4.8 stars rating in Capterra and 4.7 in G2Crowd, Excel needs a category on its own since this powerful tool has been in the hands of analysts for a very long time. Often considered a traditional size of analysis, Excel is still widely used across the globe. The reasons are fairly simple: there aren’t many people who have never used it or come across it at least once in their career. It’s a fairly versatile data analyst tool where you simply manipulate rows & columns to lớn create your analysis. Once this part is finished, you can export your data và send it khổng lồ the desired recipients, hence, you can use Excel as a reporting tool as well. You bởi need lớn update the data on your own, Excel doesn’t have an automation feature similar lớn other tools on our list. Creating pivot tables, managing smaller amounts of data & tinkering with the tabular size of analysis, Excel has developed as an electronic version of the accounting worksheet khổng lồ one of the most spread tools for data analysts.

A wide range of functionalities accompany Excel, from arranging to manipulating, calculating & evaluating quantitative data to lớn building complex equations và using pivot tables, conditional formatting, adding multiple rows and creating charts và graphs – Excel has definitely earned its place in traditional data management.

13. Industry-specific analytics tools

While there are many data analysis tools on this các mục that are used in various industries và are applied daily in analysts’ workflow, there are solutions that are specifically developed to lớn accommodate a single industry and cannot be used in another. For that reason, we have decided lớn include of one these solutions on our list, although there are many others, industry-specific data analysis programs and software. Here we focus on Qualtrics as one of the leading research software that is used by over 11000 world’s brands and has over 2M users across the globe as well as many industry-specific features focused on market research.




5 main experience features: design, customer, brand, employee, và product

Additional research services by their in-house experts

Advanced statistical analysis with their Stats i
Q analysis tool

Qualtrics is a software for data analysis that is focused on experience management (XM) and is used for market research by companies across the globe. The tool, which has a positive 4.8 stars rating on Capterra and 4.4 in G2Crowd, offers 5 product pillars for enterprise XM which include design, customer, brand, employee, and product experiences, as well as additional research services performed by their own experts. Their XM platform consists of a directory, automated actions, Qualtrics i
Q tool, and platform security features that combine automated và integrated workflows into a single point of access. That way, users can refine each stakeholder’s experience & use their tool as an “ultimate listening system.”

Since automation is becoming increasingly important in our data-driven age, Qualtrics has also developed drag-and-drop integrations into the systems that companies already use such as CRM, ticketing, or messaging, while enabling users khổng lồ deliver automatic notifications khổng lồ the right people. This feature works across brand tracking & product feedback as well as customer và employee experience. Other critical features such as the directory where users can connect data from 130 channels (including web, SMS, voice, video, or social), & Qualtrics i
Q to analyze unstructured data will enable users khổng lồ utilize their predictive analytics engine and build detailed customer journeys. If you’re looking for a data analytic software that needs to lớn take care of market research of your company, Qualtrics is worth the try.

14. Data science platforms

Data science can be used for most software solutions on our list, but it does deserve a special category since it has developed into one of the most sought-after skills of the decade. No matter if you need to lớn utilize preparation, integration or data analyst reporting tools, data science platforms will probably be high on your danh sách for simplifying analytical processes and utilizing advanced analytics models lớn generate in-depth data science insights. Lớn put this into perspective, we will present Rapid
Miner as one of the top data analyst software that combines deep but simplified analysis.




A comprehensive data science and machine learning platform with 1500+ algorithms và functions

Possible to integrate with Python và R as well as support for database connections (e.g. Oracle)

Advanced analytics features for descriptive and prescriptive analytics

Miner, which was just acquired by Altair in 2022 as a part of their data analytics portfolio, is a tool used by data scientists across the world lớn prepare data, utilize machine learning, and model operations in more than 40 000 organizations that heavily rely on analytics in their operations. By unifying the entire data science cycle, Rapid
Miner is built on 5 bộ vi xử lý core platforms and 3 automated data science products that help in the design và deployment of analytics processes. Their data exploration features such as visualizations và descriptive statistics will enable you to get the information you need while predictive analytics will help you in cases such as churn prevention, risk modeling, text mining, & customer segmentation.

With more than 1500 algorithms & data functions, tư vấn for 3rd các buổi tiệc nhỏ machine learning libraries, integration with Python or R, & advanced analytics, Rapid
Miner has developed into a data science platform for deep analytical purposes. Additionally, comprehensive tutorials and full automation, where needed, will ensure simplified processes if your company requires them, so you don’t need khổng lồ perform manual analysis. All these positive traits have earned the tool a positive 4.4 stars rating on Capterra and 4.6 stars in G2Crowd. If you’re looking for analyst tools và software focused on deep data science management & machine learning, then Rapid
Miner should be high on your list.


The amount of data being produced is only getting bigger, hence, the possibility of it involving errors. To lớn help analysts avoid these errors that can damage the entire analysis process is that data cleansing solutions were developed. These tools help in preparing the data by eliminating errors, inconsistencies, & duplications enabling users khổng lồ extract accurate conclusions from it. Before cleansing platforms were a thing, analysts would manually clean the data, this is also a dangerous practice since the human eye is prompt khổng lồ error. That said, powerful cleansing solutions have proved lớn boost efficiency và productivity while providing a competitive advantage as data becomes reliable. The cleansing software we picked for this section is a popular solution named Open




Data explorer to lớn clean “messy” data using transformations, facets, & clustering, among others

Transform data to lớn the format you desire, for example, turn a các mục into a table by importing the tệp tin into Open

Includes a large danh mục of extensions & plugins to liên kết and extend datasets with various web services

Previously known as Google Refine, Open
Refine is a Java-based open-source desktop application for working with large sets of data that needs to be cleaned. The tool, with ratings of 4.0 stars in Capterra and 4.6 in G2Crowd, also enables users lớn transform their data from one format khổng lồ another and extend it with website services & external data. Open
Refine has a similar interface khổng lồ the one of spreadsheet applications và can handle CSV file formats, but all in all, it behaves more as a database. Upload your datasets into the tool và use their multiple cleaning features that will let you spot anything from extra spaces khổng lồ duplicated fields.

Available in more than 15 languages, one of the main principles of Open
Refine is privacy. The tool works by running a small hệ thống on your computer & your data will never leave that vps unless you decide to share it with someone else.


Next, in our insightful danh mục of data analyst tools we are going to lớn touch on data mining. In short, data mining is an interdisciplinary subfield of computer science that uses a set of statistics, artificial intelligence and machine learning techniques và platforms khổng lồ identify hidden trends & patterns in large, complex data sets. To bởi vì so, analysts have to perform various tasks including data classification, cluster analysis, association analysis, regression analysis, và predictive analytics using professional data mining software. Businesses rely on these platforms to lớn anticipate future issues and mitigate risks, make informed decisions to lớn plan their future strategies, & identify new opportunities khổng lồ grow. There are multiple data mining solutions in the market at the moment, most of them relying on automation as a key feature. We will focus on Orange, one of the leading mining software at the moment.




Visual programming interface to lớn easily perform data mining tasks via drag and drop

Multiple widgets offering a mix of data analytics & machine learning functionalities

Add-ons for text mining và natural language processing to lớn extract insights from text data

Orange is an mở cửa source data mining & machine learning tool that has existed for more than 20 years as a project from the University of Ljubljana. The tool offers a set of data mining features, which can be used via visual programming or Python Scripting, as well as other data analytics functionalities for simple và complex analytical scenarios. It works under a “canvas interface” in which users place different widgets khổng lồ create a data analysis workflow. These widgets offer different functionalities such as reading the data, inputting the data, filtering it, & visualizing it, as well as setting machine learning algorithms for classification và regression, among other things.

What makes this software so popular amongst others in the same category is the fact that it provides beginners và expert users with a pleasant usage experience, especially when it comes khổng lồ generating swift data visualizations in a quick & uncomplicated way. Orange, which has 4.2 stars ratings on both Capterra & G2Crowd, offers users multiple online tutorials lớn get them acquainted with the platform. Additionally, the software learns from the user’s preferences and reacts accordingly, this is one of their most praised functionalities.

17. Data visualization platforms

Data visualization has become one of the most indispensable elements of data analytics tools. If you’re an analyst, there is probably a strong chance you had khổng lồ develop a visual representation of your analysis or utilize some size of data visualization at some point. Here we need lớn make clear that there are differences between professional data visualization tools often integrated through already mentioned BI tools, không tính tiền available solutions as well as paid charting libraries. They’re simply not the same. Also, if you look at data visualization in a broad sense, Excel & Power
Point also have it on offer, but they simply cannot meet the advanced requirements of a data analyst who usually chooses professional BI or data viz tools as well as modern charting libraries, as mentioned. We will take a closer look at Highcharts as one of the most popular charting libraries on the market.




Interactive Java
Script library compatible with all major web browsers and mobile systems lượt thích Android & i

Designed mostly for a technical-based audience (developers)

GL-powered boost module to render millions of datapoints directly in the browser

Highcharts is a multi-platform library that is designed for developers looking to showroom interactive charts to lớn web and mobile projects. With a promising 4.6 stars rating in Capterra and 4.5 in G2Crowd, this charting library works with any back-end database and data can be given in CSV, JSON, or updated live. They also feature intelligent responsiveness that fits the desired chart into the dimensions of the specific container but also places non-graph elements in the optimal location automatically.

Highcharts supports line, spline, area, column, bar, pie, scatter charts and many others that help developers in their online-based projects. Additionally, their Web
GL-powered boost module enables you khổng lồ render millions of datapoints in the browser. As far as the source code is concerned, they allow you to download and make your own edits, no matter if you use their không tính tiền or commercial license. In essence, Basically, Highcharts is designed mostly for the technical target group so you should familiarize yourself with developers’ workflow and their Java
Script charting engine. If you’re looking for a more easy to use but still powerful solution, you might want to consider an online data visualization tool like datapine.

3) Key Takeaways và Guidance

We have explained what are data analyst tools and given a brief mô tả tìm kiếm of each to provide you with the insights needed lớn choose the one (or several) that would fit your analytical processes the best. We focused on diversity in presenting tools that would fit technically skilled analysts such as R Studio, Python, or My
SQL Workbench. On the other hand, data analysis software like datapine cover needs both for data analysts và business users alike so we tried to lớn cover multiple perspectives và skill levels.

We hope that by now you have a clearer perspective on how modern solutions can help analysts perform their jobs more efficiently in a less prompt khổng lồ error environment. Lớn conclude, if you want to start an exciting analytical journey and test a professional BI analytics software for yourself, you can try datapine for a 14-day trial, completely không tính phí of charge and with no hidden costs.

Big data analytics perform batch analysis and processing on storeddata such as data in a feature layer or cloud big data stores such as
Amazon S3 và Azure Blob Store. Big data analytics are typically used for summarizing observations, performing pattern analysis, và enriching data. The analysis that can be performeduses tools from the following tool categories in Velocity:Analyze Patterns
Data Enrichment
Find Locations
Manage Data
Summarize Data
Use Proximity


As an environmental scientist, you can identify times và locations of high ozone levels across the country in a dataset of millions of static sensor records.As a retail analyst, you can process millions of anonymous cell phone locations within a designated time range lớn determine the number of potential consumers within a certain distance of store locations.As a GIS analyst, you can run a recurring big data analytic that checks a data source for new features every five minutes và sends a notification if certain attribute or spatial conditions are met.

Components of a big data analytic

There are three components of a big data analytic:Sources
There can be multiple data sources in an analytic.Tools
Tools process or analyze data that is loaded from sources.There can be multiple tools in a big data analytic.Tools can be connected to each other where the output of one tool represents the input of the next tool.Outputs
An đầu ra defines what should be done with the results of the big dataanalytic processing.The result of a tool or source can be sent lớn multiple outputs.

Work with outputs

When a real-time or big data analytic is run, it will generate one or more outputs. Depending on the type of outputs configured, there are several ways you can access & interact with those outputs in Arc
GIS Velocity.

GIS feature layer và stream layer outputs

When a real-time or big data analytic creates a feature layer or stream layer output, you can interact with those output layers in Velocity. Chú ý that these methods are not available if the analytic has not yet run.

Access feature layer và stream layer outputs in the analytic

When editing an analytic that has run và successfully created output layers, right-click a feature or stream layer node in the analytic editor to view available options including accessing the node"s properties, changing the node label, viewing the item details, opening the layer in a maps viewer or scene viewer, sampling the node data, removing the node, and more.

Access feature layer và stream layer outputs from the Layers page

All feature layers, map image layers, và stream layers created by real-time and big data analytics will appear on the Layers page in Velocity. From here, you can edit existing layers, view these layers in a maps viewer, access & view the tác phẩm details, open the layer in the REST Services Directory, as well as delete & share the layers.

Amazon S3 and Azure Blob Store outputs

Big data analytics are capable of writing đầu ra features to lớn Amazon S3 or Azure Blob Store cloud storage. Once the big data analytic finishes, the data will be available in the respective cloud location. If you vì not see the đầu ra as expected, kiểm tra the analytic logs from the Logs tab.

All other outputs

Other đầu ra types for big data analytics include email and Kafka. With these outputs, Velocity establishes a connection with the chosen output & sends the sự kiện data khổng lồ the output accordingly.

Run a big data analytic (schedule)

Big data analytics can be configured to lớn run in one of two ways, they can be run once or they can be scheduled to run. When making changes khổng lồ the run settings, remember lớn click Apply khổng lồ save the changes khổng lồ the big data analytic.

Runs once

Big data analytics configured lớn run once only run when you start the big data analytic. The analytic performs the processing and analysis as defined and reverts lớn a stopped state once complete. This differs from feeds, real-time analytics, & scheduled big data analytics, which all continue lớn run once started. Runs once is the default option for big data analytics.



A big data analytic can be scheduled lớn run periodically (for example, every five minutes) or at a recurring time (for example, daily at 4 a.m.).


When a big data analytic is configured to lớn run in a scheduled manner, once the analytic is started, it will remain started unless the analytic is stopped. Unlike a real-time analytic, a scheduled big data analytic that is started will only consume resources while it is performing the analysis. For example, if a big data analytic is scheduled to run periodically every hour, and the analysis takes four minutes to lớn complete, the big data analytic will only consume resources once an hour for the four minutes that it takes khổng lồ perform the analysis.

For more information on how lớn schedule big data analytics, see Schedule recurring big data analysis.

Perform near-real-time analysis

Scheduled big data analytics can be used lớn perform near-real-time analysis in which the big data analytic processes only the latest features added khổng lồ a feature layer since its last run. For more information, use cases, and options for configuring near-real-time analysis, see Perform near real-time analysis.

Generate up-to-date informational products

Alternatively, scheduled big data analytics can be used lớn generate up-to-date informational products at a user-defined interval. For more information and examples of use cases & options for such workflows, see Generate up-to-date informational products.

Run settings

With big data analytics, you can adjust the Run settings. These settings control the resource allocation provided by your Velocity deployment to your analytic for processing. Remember khổng lồ save your analytic after making any changes to lớn run settings.

Generally, the more resources provided lớn an analytic, the faster it will complete processing và generate results. When working with larger datasets or complex analysis, it is a best practice, và at times essential, to lớn increase the resource allocation available khổng lồ an analytic.

Xem thêm: Phân tích hình tượng người đàn bà hàng chài chọn lọc hay nhất

Conversely, if you have a simple analytic with few features that runs successfully with the Medium (default) setting, consider decreasing the run settings resource allocation khổng lồ the Small setting. This allows you khổng lồ run more feeds, real-time analytics, & big data analytics in your Velocity deployment.


Considerations and limitations

There are several considerations khổng lồ keep in mind when using big data analytics:

Big data analytics are optimized for working with high volumes of data and summarizing patterns and trends, which typically result in a reduced phối of output features or records compared to the number of input features.Big data analytics are not optimized for loading or writing massive volumes of features in a single run. Writing tens of millions of features or higher with a big data analytic may result in longer run times.As a best practice, it is recommended that you use big data analytics for summarization and analysis as opposed to copying data.

Feedback on this topic?